Source code for langchain_community.embeddings.minimax

from __future__ import annotations

import logging
from typing import Any, Callable, Dict, List, Optional

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from tenacity import (

logger = logging.getLogger(__name__)

def _create_retry_decorator() -> Callable[[Any], Any]:
    """Returns a tenacity retry decorator."""

    multiplier = 1
    min_seconds = 1
    max_seconds = 4
    max_retries = 6

    return retry(
        wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
        before_sleep=before_sleep_log(logger, logging.WARNING),

[docs]def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator() @retry_decorator def _embed_with_retry(*args: Any, **kwargs: Any) -> Any: return embeddings.embed(*args, **kwargs) return _embed_with_retry(*args, **kwargs)
[docs]class MiniMaxEmbeddings(BaseModel, Embeddings): """MiniMax's embedding service. To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and ``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.embeddings import MiniMaxEmbeddings embeddings = MiniMaxEmbeddings() query_text = "This is a test query." query_result = embeddings.embed_query(query_text) document_text = "This is a test document." document_result = embeddings.embed_documents([document_text]) """ endpoint_url: str = "" """Endpoint URL to use.""" model: str = "embo-01" """Embeddings model name to use.""" embed_type_db: str = "db" """For embed_documents""" embed_type_query: str = "query" """For embed_query""" minimax_group_id: Optional[str] = None """Group ID for MiniMax API.""" minimax_api_key: Optional[SecretStr] = None """API Key for MiniMax API.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that group id and api key exists in environment.""" minimax_group_id = get_from_dict_or_env( values, "minimax_group_id", "MINIMAX_GROUP_ID" ) minimax_api_key = convert_to_secret_str( get_from_dict_or_env(values, "minimax_api_key", "MINIMAX_API_KEY") ) values["minimax_group_id"] = minimax_group_id values["minimax_api_key"] = minimax_api_key return values
[docs] def embed( self, texts: List[str], embed_type: str, ) -> List[List[float]]: payload = { "model": self.model, "type": embed_type, "texts": texts, } # HTTP headers for authorization headers = { "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}", # type: ignore[union-attr] "Content-Type": "application/json", } params = { "GroupId": self.minimax_group_id, } # send request response = self.endpoint_url, params=params, headers=headers, json=payload ) parsed_response = response.json() # check for errors if parsed_response["base_resp"]["status_code"] != 0: raise ValueError( f"MiniMax API returned an error: {parsed_response['base_resp']}" ) embeddings = parsed_response["vectors"] return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed documents using a MiniMax embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ embeddings = embed_with_retry(self, texts=texts, embed_type=self.embed_type_db) return embeddings
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using a MiniMax embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. """ embeddings = embed_with_retry( self, texts=[text], embed_type=self.embed_type_query ) return embeddings[0]